TensorFlow VPS Hosting — from €5.49/mo
Train TensorFlow models without limits — a server on NVMe with RAM to spare, 5-hour test period, so you risk nothing
Launch TensorFlow now
A TensorFlow VPS is a virtual server with full root access for training models and running inference on TensorFlow and Keras. You install Linux, Python and the TensorFlow versions you need, and the compute runs on the server, freeing up your computer. Spare RAM and fast NVMe drives keep datasets and model weights close, and TensorBoard opens in your browser. Plans start at €5.49/mo, with a 5-hour test period — check the performance on your own data before paying.
Plans
VPS for TensorFlow
Standard 1 (4 vCPU, 4 GB) and Standard 2 (4 vCPU, 6 GB) suit prototypes and debugging. CPU inference and small-model training need Power 3 (16 vCPU, 24 GB) or higher; heavy training needs a graphics card. 5-hour test period before payment.
Compare all plansStandard 1
| 4 vCPU · AMD EPYC |
| 4 GB RAM |
| 50 GB NVMe |
| Unlimited traffic |
| 1× IPv4 · KVM |
| 24/7 support |
Standard 2
| 4 vCPU · AMD EPYC |
| 6 GB RAM |
| 60 GB NVMe |
| Unlimited traffic |
| 1× IPv4 · KVM |
| 24/7 support |
Your own TensorFlow environment
Install the Python, TensorFlow and Keras versions you need — for your models, with no external limits or compute-time caps.
Training in the cloud
Long training runs go on the server around the clock without tying up your laptop — start them and leave them computing.
Datasets on NVMe
Fast drives and spare RAM speed up loading large datasets and keep model weights next to the compute.
How to run TensorFlow on a VPS
Choose a plan and an image
Pick a plan — Standard 1 (4 vCPU, 4 GB) or Standard 2 (4 vCPU, 6 GB) — and select a Linux image, Ubuntu or Debian, for your ML stack.
Install the OS yourself
Through the panel you deploy the chosen OS on a KVM server yourself — the environment stays fully under your control.
Set up TensorFlow
Over SSH or the KVM console install Python and TensorFlow, upload your data and start training with TensorBoard.
A server for your models
We will suggest which plan fits your model and dataset size: RAM volume, vCPU count and NVMe space. For GPU inference and training we will put together a configuration separately.
We will help set up the environment, TensorBoard and secure access. The alternative framework is a PyTorch VPS; for GPU configurations see GPU servers.
Help me size itFrequently asked questions
Didn't find your answer?
Write to us via the contact page.
Any — you have root access and install the Python, TensorFlow and Keras versions your project needs yourself.
Yes: start training via systemd, tmux or nohup — the process continues after you disconnect from SSH. Logs and checkpoints are written to the server disk.
The base plans focus on vCPU, RAM and NVMe. For GPU workloads we will put together a configuration separately — send us a request.
Yes. Once TensorBoard is running it opens in your browser at the server address — watch the training metrics remotely.
For inference and training small models — yes: 16 vCPU and 24 GB (Power 3, €27.99/mo) handle typical workloads. Heavy training requires a GPU.
Reduce the batch size and use data generators instead of loading the whole dataset into RAM. If that is not enough, move to a plan with 32–64 GB (Max 1 or Max 3).
Yes, the notebook is installed on the server and opened over a secure tunnel or behind Nginx with a password. Do not expose the port without protection.
Yes, the KVM console gives the same access as a monitor and keyboard next to the machine: editing the configuration, booting into recovery, reinstalling.